【正文】 變成典型事 例 ,首先它必須要求改成 000,前面的自動(dòng)變成參考事例 . 事例索引碼的結(jié)構(gòu)如圖 1 所示。 事例的層次化 夾具的結(jié)構(gòu)相似被認(rèn)為是整個(gè)夾具，成分和內(nèi)容相似。成分事例（夾具的最低層）是功能成分 和和其他成分的結(jié)構(gòu)。根據(jù)夾具設(shè)計(jì)的需求，事例修改的策略使最接近的事例方法和知識(shí)指導(dǎo)結(jié)合起來。 修改方法： (1)根據(jù)夾具事例庫的事例索引信息，查找有關(guān)事例庫。相似點(diǎn)尺寸的計(jì)算依靠特征的類型。同時(shí)， ，數(shù)值計(jì)算如下： 其中 表示第 i 個(gè)特征的索引特性值， 表示事例庫中第 j 個(gè)事例的第 i 個(gè)特征的特性值。 事例庫最初建立的時(shí)候，只有一些事例，數(shù)值可以建小一點(diǎn)。 事例的存儲(chǔ) 在將一個(gè)新的事例保存到事例庫之前，設(shè)計(jì)者必須考慮保存是否 有價(jià)值。一個(gè)同族事例是有事例庫中索引碼前 13 位相同而最后三位不同的那些事例組成的?；蛘咧荒茏鳛橐粋€(gè)參考事例保存（最后三位不是“ 000”，而且和其它的不一樣）在事例庫中。然后，輸入或設(shè)計(jì)工件的模型。材料是 45 鋼，底座，形狀為塊狀，生產(chǎn)批量為中批等。 表 2 最相似事例的事例索引碼 特征名稱 特性值 索引碼 夾具類型 車床夾具 1 工件形狀 塊狀 9 工件材料 中碳鋼 3 批量 中批 2 工件比例 小 5 工件重量 輕 5 工件剛度 硬度強(qiáng) 1 加工內(nèi)容 孔 3 程序要求 完成加工 3 定位方法 三個(gè)平面 1 夾緊方法 不確定 ？ 夾具體 復(fù)合 4 其他 沒有 0 相似點(diǎn)的計(jì)算如下： 11 所以夾具的相似尺寸值是 ，這是在事例庫中用于設(shè)計(jì)的最相似的事例，最相似的事例的結(jié)構(gòu)如圖 4 所示 圖 4 最相似的夾具 當(dāng)成分替代，修改定位模型和夾緊模型，以及調(diào)節(jié)相關(guān)尺寸之后，新的夾具被設(shè)計(jì)出來，圖形如圖 6 所示 12 圖 5 需要設(shè)計(jì)的新夾具 因?yàn)樵谑吕龓熘袥]有相似夾具，新夾具被儲(chǔ)存到事例庫中。基于事例推理中提出的一個(gè)夾具設(shè)計(jì)的框架已經(jīng)被實(shí)行了，使用的是支持基礎(chǔ)數(shù)據(jù)的 VC++， UG 電腦繪圖軟件。fixture design。 3)Sort the order of similarity measure, the biggest value, which is the most analogical case. Similarity between two cases is based on the similarity between the two cases. features. The calculation of similarity measure depends on the type of the feature. The value of similarity can be calculated for numerical values, for example, pare Work piece with the weight of 50kg and 20kg. The value can also be calculated between non numerical values, for example, now the first 13 digits index code is all non numerical values. The similarity measure of a fixture is calculated as follows: 19 where S is the similarity measure of current fixture, n is the number of the index feature, is the weight of each feature, is the similarity measure of the attribute of the i2th feature with the attribute of relative feature of the jth case in the case library. At the same time, , the value counts as follows: . Where is the value of the index attribute of the ith feature, and is the value of attribute of the relative ith feature of the jth case in case library. So there are two methods to select the analogical fixture. One is to set the value. If the values of similarity measure of current cases were less than a given value, those cases would not be selected as analogical cases. When the case library is initially set up, and there are only a few cases, the value can be set smaller. If there are lots of analogical cases, the value should get larger. The other is just to set the number of the analogical cases (such as10), which is the largest value of similarity measure from the sorted order. 3 Case adaptation and Case Storage 20 Case adaptation The modification of the analogical case in the fixture design includes the following three cases: 1) The substitution of ponents and the ponent。 and it supports a better transfer and explanation of new knowledge that is more different than the rule library. A proposed fixture design framework on the CBR has been implemented by using Visual C ++, UG/Open API in U n graphics with Oracle as database support, which also has been integrated with the 32D parametric mon ponent library, mon ponents library and typical fixture library. The prototype system, developed here, is used for the aviation project, and aids the fixture designers to improve the design efficiency and reuse previous design resources. 。 3) The redesign of the model. If the ponents and ponent of the fixture are mon objects, they can be edited, substituted and